Abstract The cooccurrence of multi disease among children and adolescents has become increasingly prominent, necessitating a shift from "single disease separate treatment" to "multi disease co prevention". Focusing on four common health problems in children and adolescents, namely, myopia, depression, overweight/obesity, and spinal abnormalities, the paper systematically reviews the association between four categories of factors, society, environment, family, and individual behavior, and the co occurrence of the aforementioned health problems. The findings indicate that particulate matter 2.5 exposure, light at night, physical inactivity, excessive screen time, and unhealthy diet constitute the primary cross dimensional risk network. Based on these findings, the paper proposes a collaborative action framework involving family, school, and society, emphasizing systematic strategies ranging from upstream environmental governance to downstream behavioral intervention, so as to provide evidence for the practice and policy making of multi disease co-prevention in children and adolescents.

Digital polymerase chain reaction(dPCR)enables absolute quantitative detection of nucleic acid samples.Since the quantitative upper and lower limits of detectable samples mainly depend on the volume and number of single droplets,the abundance of the sample to be tested,the volume of single droplets,and the number of droplets need to be adapted.For samples with unknown abundance,repeated adjustment of droplet size is not allowed.In this study,a one-step double-volume droplet generation method was proposed,and a double-volume droplet microfluidic chip was developed to verify the quantitative detection capability of the chip using a duck-derived kit.The results showed that the droplets with different volumes had different quantitative capabilities.Large-volume droplets had higher reliability for low-abundance sample concentrations,while small-volume droplets had advantages in detecting high-abundance sample concentrations.The double-volume droplets produced by the double-volume droplet microfluidic chip proposed in this study greatly improved the reliability of quantitative capabilities,and had broad application prospects in detection of precious nucleic acid samples with unknown abundance in the field of microfluidic PCR.

The structure of intestinal tissue is complex. In vitro simulation of intestinal structure and function is important for studying intestinal development and diseases. Recently, organoids have been successfully constructed and they have come to play an important role in biomedical research. Organoids are miniaturized three-dimensional (3D) organs, derived from stem cells, which mimic the structure, cell types, and physiological functions of an organ, making them robust models for biomedical research. Intestinal organoids are 3D micro-organs derived from intestinal stem cells or pluripotent stem cells that can successfully simulate the complex structure and function of the intestine, thereby providing a valuable platform for intestinal development and disease research. In this article, we review the latest progress in the construction and application of intestinal organoids.
Organoids/cytology* ; Intestines/physiology* ; Humans ; Animals ; Pluripotent Stem Cells

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To evaluate the diagnostic performance of the minimum-cost-path-based CT angiography-derived fractional flow reserve(MCP-FFR)and the deep learning-driven CT angiography-derived fractional flow reserve(DeepCL-FFR),and to particularly explore the potential value of the DeepCL algorithm in improving diagnostic accuracy within the"gray zone."Methods A retrospective analysis was conducted on 151 coronary vessels from 109 patients with coronary artery disease,who were hospitalized at the General Hospital of the People's Liberation Army between January 2020 and June 2021.Pearson correlation and Bland-Altman plots were employed to assess the correlation and agreement of the two CT-FFR methods with invasive FFR.A CT-FFR range of 0.70-0.80 was defined as the diagnostic"gray zone."The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value for detecting hemodynamic abnormalities were calculated and analyzed.The DeLong test was used to compare the areas under the receiver operating characteristic curves(AUC)between the two CT-FFR calculation methods.Results Both CT-FFR methods exhibited a positive correlation with invasive FFR(MCP-FFR:r=0.75,P＜0.001;DeepCL-FFR:r=0.86,P＜0.001)and showed good agreement(MCP-FFR:mean difference=0.010,P=0.351;DeepCL-FFR:mean difference=-0.003,P=0.772).Both DeepCL-FFR(AUC 0.97,95％CI 0.94-0.99)and MCP-FFR(AUC 0.92,95％CI 0.88-0.97)demonstrated favorable diagnostic performance for detecting hemodynamic abnormalities(P=0.122).In the"gray zone"for hemodynamic abnormality,the diagnostic accuracy of MCP-FFR was 68.8％,whereas DeepCL-FFR increased it to 89.7％.DeepCL-FFR also exhibited superior diagnostic performance(AUC 0.89,95％CI 0.73-0.99)within the"gray zone,"which was significantly higher than that of MCP-FFR(AUC 0.71,95％CI 0.54-0.87)(P＜0.001).Conclusions The deep learning-driven coronary centerline extraction algorithm,DeepCL,demonstrates superior diagnostic performance in CT-FFR for detecting hemodynamic abnormalities,particularly by significantly improving diagnostic accuracy in the"gray zone."

Liposomes serve as critical carriers for drugs and vaccines,with their biological effects influenced by their size.The microfluidic method,renowned for its precise control,reproducibility,and scalability,has been widely employed for liposome preparation.Although some studies have explored factors affecting liposomal size in microfluidic processes,most focus on small-sized liposomes,predominantly through experimental data analysis.However,the production of larger liposomes,which are equally significant,remains underexplored.In this work,we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning(ML)model capable of accurately predicting liposomal size.Experimental validation was conducted using a staggered herringbone micromixer(SHM)chip.Our findings reveal that most investigated variables significantly influence liposomal size,often interrelating in complex ways.We evaluated the predictive performance of several widely-used ML algorithms,including ensemble methods,through cross-validation(CV)for both lipo-some size and polydispersity index(PDI).A standalone dataset was experimentally validated to assess the accuracy of the ML predictions,with results indicating that ensemble algorithms provided the most reliable predictions.Specifically,gradient boosting was selected for size prediction,while random forest was employed for PDI prediction.We successfully produced uniform large(600 nm)and small(100 nm)liposomes using the optimised experimental conditions derived from the ML models.In conclusion,this study presents a robust methodology that enables precise control over liposome size distribution,of-fering valuable insights for medicinal research applications.

Objective: To explore the association between mental health and muscle strength among Chinese adolescents aged 13- 18, providing a theoretical foundation and intervention strategies for mental health promotion. Methods: Data were obtained from the 2019 Chinese National Survey on Students Constitution and Health, including 98 631 Chinese adolescents aged 13- 18. Psychological distress was assessed by using the Kessler Psychological Distress Scale (K10), and mental well being was measured with the Warwick-Edinburgh Mental Well being Scale (WEMWBS). Based on the gender and age specific Z scores of various test items [grip strength, standing long jump, pull ups (for males), and sit ups (for females)], muscle strength index (MSI) was constructed to evaluate the comprehensive level of muscle strength in adolescents. According to the Dual factor Model (DFM) of mental health, participants were categorized into four groups:troubled, symptomatic but content, vulnerable, and complete mental health. Gender differences were analyzed by using Chi-square tests, trends were tested with Cochran-Armitage tests, and multinomial Logistic regression models were applied to assess associations between muscle strength and mental health among adolescents. Results: In 2019, 37.4% of Chinese adolescents aged 13-18 were reported of high mental distress, and 59.9% were reported of low mental well being. Boys had significantly lower rates of high mental distress (35.3%) and low mental well being (55.6%) compared to girls (39.4%, 64.3%), and the differences were of statistical significance ( χ 2=176.13, 780.42, both P <0.05). In 2019, the rate of complete mental health among adolescents showed a downward trend with increasing age ( χ 2 trend = 258.47) and a gradual upward trend with increasing muscle strength levels ( χ 2 trend =123.14)，and both boys and girls exhibited similar trends ( χ 2 trend =103.83, 168.46; 57.00 , 67.34) (all P <0.05). The results of the unordered multiclass Logistic regression model showed that after controlling for confounding factors such as age and gender, when the completely pathological group as a reference, for every 1 unit increase in MSI in adolescents, the likelihood of being in a completely mental health state increased by 29% ( OR = 1.29); for every unit increase in the Z-score for pull ups, the likelihood of being in a completely mental health state increased by 6% ( OR =1.06) among boys; for every 1 unit increase in sit up Z score, the likelihood of being in a completely mental health state increased by 19% ( OR =1.19) among girls (all P <0.05). Conclusions The mental health status of Chinese adolescents is not good enough. Muscle strength is positively associated with mental health.

Liposomes serve as critical carriers for drugs and vaccines, with their biological effects influenced by their size. The microfluidic method, renowned for its precise control, reproducibility, and scalability, has been widely employed for liposome preparation. Although some studies have explored factors affecting liposomal size in microfluidic processes, most focus on small-sized liposomes, predominantly through experimental data analysis. However, the production of larger liposomes, which are equally significant, remains underexplored. In this work, we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning (ML) model capable of accurately predicting liposomal size. Experimental validation was conducted using a staggered herringbone micromixer (SHM) chip. Our findings reveal that most investigated variables significantly influence liposomal size, often interrelating in complex ways. We evaluated the predictive performance of several widely-used ML algorithms, including ensemble methods, through cross-validation (CV) for both liposome size and polydispersity index (PDI). A standalone dataset was experimentally validated to assess the accuracy of the ML predictions, with results indicating that ensemble algorithms provided the most reliable predictions. Specifically, gradient boosting was selected for size prediction, while random forest was employed for PDI prediction. We successfully produced uniform large (600 nm) and small (100 nm) liposomes using the optimised experimental conditions derived from the ML models. In conclusion, this study presents a robust methodology that enables precise control over liposome size distribution, offering valuable insights for medicinal research applications.

BACKGROUND: The urban-rural disparities in overweight and obesity among children and adolescents are narrowing, and there is a need for long-term and updated data to explain this inequality, understand the underlying mechanisms, and identify priority groups for interventions. METHODS: We analyzed data from seven rounds of the Chinese National Survey on Students Constitution and Health (CNSSCH) conducted from 1985 to 2019, focusing on school-age children and adolescents aged 7-18 years. Joinpoint regression was used to identify inflection points (indicating a change in the trend) in the prevalence of overweight and obesity during the study period, stratified by urban/rural areas and sex. Annual percent change (APC), average annual percent change (AAPC), and 95% confidence interval (CI) were used to describe changes in the prevalence of overweight and obesity. Polynomial regression models were used to predict the prevalence of overweight and obesity among children and adolescents in 2025 and 2030, considering urban/rural areas, sex, and age groups. RESULTS: The prevalence of overweight and obesity in urban boys and girls showed an inflection point of 2000, with AAPC values of 10.09% (95% CI: 7.33-12.92%, t = 7.414, P <0.001) and 8.67% (95% CI: 6.10-11.30%, t = 6.809, P <0.001), respectively. The APC for urban boys decreased from 18.31% (95% CI: 4.72-33.67%, t = 5.926, P = 0.027) to 4.01% (95% CI: 1.33-6.75%, t = 6.486, P = 0.023), while the APC for urban girls decreased from 13.88% (95% CI: 1.82-27.38%, t = 4.994, P = 0.038) to 4.72% (95% CI: 1.43-8.12%, t = 6.215, P = 0.025). However, no inflection points were observed in the best-fit models for rural boys and girls during the period 1985-2019. The prevalence of overweight and obesity for both urban and rural boys is expected to converge at 35.76% by approximately 2027. A similar pattern is observed for urban and rural girls, with a prevalence of overweight and obesity reaching 20.86% in 2025. CONCLUSIONS The prevalence of overweight and obesity among Chinese children and adolescents has been steadily increasing from 1985 to 2019. A complete reversal in urban-rural prevalence is expected by 2027, with a higher prevalence of overweight and obesity in rural areas. Urgent action is needed to address health inequities and increase investments, particularly policies targeting rural children and adolescents.
Humans ; Child ; Adolescent ; Female ; Male ; Rural Population/statistics & numerical data* ; Overweight/epidemiology* ; Prevalence ; China/epidemiology* ; Pediatric Obesity/epidemiology* ; Obesity/epidemiology* ; Urban Population

Cardiovascular diseases are the leading cause of mortality, posing a significant threat to human health due to the high incidence rate. Atherosclerosis, a chronic inflammatory disease, serves as the primary pathological basis for most such conditions. The incidence of atherosclerosis continues to rise, but its pathogenesis has not been fully elucidated. As an important member of the small GTPase superfamily, Ras-association proximate 1 (Rap1) is an important molecular switch involved in the regulation of multiple physiological functions including cell differentiation, proliferation, and adhesion. Rap1 achieves the utility of the molecular switch by cycling between Rap1-GTP and Rap1-GDP. Rap1 may influence the occurrence and development of atherosclerosis in a cell-specific manner. This article summarizes the potential role and mechanism of Rap1 in the progression of atherosclerosis in different cells, aiming to provide new therapeutic targets and strategies for clinical intervention.
Humans ; Atherosclerosis/metabolism* ; rap1 GTP-Binding Proteins/physiology* ; Animals ; Cell Differentiation ; Cell Adhesion ; Cell Proliferation